# Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry

arXiv: https://arxiv.org/abs/2103.15783 · HTML: https://mlsanity.com/paper/2103.15783/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Learning by Unsupervised Nonlinear Diffusion](https://mlsanity.com/paper/1810.06702/) (2018)
- [Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization](https://mlsanity.com/paper/1310.7441/) (2013)
- [Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion](https://mlsanity.com/paper/1704.07961/) (2017)
- [Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering](https://mlsanity.com/paper/1902.05402/) (2019)
- [Learning by Active Nonlinear Diffusion](https://mlsanity.com/paper/1905.12989/) (2019)
- [Spatially regularized active diffusion learning for high-dimensional images](https://mlsanity.com/paper/1911.02155/) (2019)
- [A Multiscale Environment for Learning by Diffusion](https://mlsanity.com/paper/2102.00500/) (2021)

## Built on by (most cited first, 7 of 7)

- [Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.15447/) (2023)
- [Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images](https://mlsanity.com/paper/2204.06298/) (2022)
- [Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images](https://mlsanity.com/paper/2203.09992/) (2022)
- [Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering](https://mlsanity.com/paper/2204.09041/) (2022)
- [Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry](https://mlsanity.com/paper/2204.13497/) (2022)
- [A signal separation view of classification](https://mlsanity.com/paper/2509.24140/) (2025)
- [Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2604.13307/) (2026)

## Abstract (arXiv)

Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized diffusion distances to efficiently and accurately learn multiple scales of latent structure in hyperspectral images. The M-SRDL clustering algorithm extracts clusterings at many scales from a hyperspectral image and outputs these clusterings' variation of information-barycenter as an exemplar for all underlying cluster structure. We show that incorporating spatial regularization into a multiscale clustering framework results in smoother and more coherent clusters when applied to hyperspectral data, yielding more accurate clustering labels.